A stochastic control framework for the design of observational brain-computer interfaces based on human error potentials
Steines, David A.
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https://hdl.handle.net/2142/24378
Description
Title
A stochastic control framework for the design of observational brain-computer interfaces based on human error potentials
Author(s)
Steines, David A.
Issue Date
2011-05-25T14:37:19Z
Director of Research (if dissertation) or Advisor (if thesis)
Coleman, Todd P.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Brain-computer interface
feedback-related negativity
Bayesian control rule
Abstract
Brain-computer interfaces (BCI) allow human subjects to interact with exogenous systems through neural signals. The goal of this interaction may be to induce behaviors or properties in either the exogenous system or the
human subject. In this thesis, we develop a novel framework for designing BCIs based on principles from adaptive control. In particular, we exploit scalp electroencephalography-derived correlates of the human error processing system to recover a subject’s desired policy for the exogenous system. This scheme allows a human subject to control a system through passive observation by critiquing actions taken by the system. We provide a necessary
and sufficient condition for convergence and simulations as a proof of concept. Further, we discuss the application of this framework to building co-adaptive BCIs and as a tool for understanding the learning process during BCI interaction.
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